Search Results for author:
Found 4 papers, 1 papers with code
Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion
Diffusion models have shown superior performance in image generation and manipulation, but the inherent stochasticity presents challenges in preserving and manipulating image content and identity.
Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching
Through extensive experiments, we demonstrate that the proposed method can accurately estimate noise models and parameters, and provide the state-of-the-art self-supervised image denoising performance in the benchmark dataset and real-world dataset.
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation
To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs text-driven image manipulation using diffusion models.
Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising
Recently, CycleGAN was shown to provide high-performance, ultra-fast denoising for low-dose X-ray computed tomography (CT) without the need for a paired training dataset.
